The Decision Sciences Sector

Posted in Operations & IT Articles, Total Reads: 1328
, Published on 27 October 2013

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The Emergence of Decision Sciences

The Decision Sciences industry, unheard of 15 years back, is creating waves nowadays. With decreasing storage costs and constantly improving technology, organizations are able to record every little thing that happens within the organization and in their interaction with customers, be it a financial transaction or a customer clicking on a particular product on their online catalogue. In very large organizations, this data can be in the order of exabytes! However, all this data is not of much use unless we can make some sense out of it. This is where Data Analysis comes into the picture.

The data analysis boom started around the early 2000’s with the managers realising that all the data their organizations recorded and stored could be a goldmine of knowledge if used well. Data Analysis aims at seeing beyond the obvious and generating insights out of massive and, in cases, redundant data sets. It aims to break down seemingly qualitative problems into quantitative ones that can be solved using the data available. Ultimately, it aims at helping organizations make better decisions.

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Science, Business or Math?

Decision Sciences is called a science because it not just entails the purely technical and quantitative analysis of the data, but also supports itself on the principles of behavioural sense and business logic. The strength and concrete solutions given by the data from unending spreadsheets is backed by subjective business sense to make decisions that are best for the organization. While subjective calls and decisions are made on a regular basis across companies, data analysis allows us a glimpse into what would not have been in our line of sight otherwise. The famous study of beer selling more when kept next to nappies is a case in point. Something like that would have never come up if not for the all-seeing data analysis methods.

How Data Analysis Works

Organizations regularly deal with problems like decrease in sales, customer attrition, failing advertisement campaigns etc. Even if a company is not facing a specific problem, it always wants to improve on how its resources are used, how cost effective its production is, how best it can gauge customer preferences etc. These apply to companies across various verticals. Each of these problems look fuzzy and vague to the common eye. Data Analysis provides a structure to them and guides us to the solution. A decision scientist chooses what data would best represent the problem at hand and how best this data could be analysed. A number of methods can be used for the analysis. While in some cases, a simple two way graph could lead to insights, some more complex problems need application of statistical principles and various assumptions based on business logic. The methods and the tools chosen generally depend on the sector, the organization in question and the problem we are dealing with.

Trends in the Decision Sciences Sector

The scope of Decision Sciences is only going to increase with more and more companies wanting to utilise their data to gain valuable insight. Big Data, which is data too big to be processed by normal methods, is a term that is already doing the rounds. Several software platforms have been developed to process such data in chunks in parallel so as to reduce computation costs and time required. Visualization of data is going to get even better than it actually is, to ensure the top management is able to interpret the insights and findings from the analysis better. Video analytics is going to get big and will be used to gauge customer behaviour while making purchases. As a customer, it will be known that every step you take within a store, every call you make to the support team, every page that you click on online is being recorded somewhere and being used to make companies function better. How that complies with the right to privacy is a something we will have to think about in the coming years.

There is a big gap between the demand and supply of decision scientists in the United States which means it is good news for the offshore analytics companies in India.